In a project with investigators from the Nervous System Development and Plasticity Section, NICHD, and the Program on Pediatric Imaging and Tissue Sciences (PPITS), NICHD we study the dynamic regulation of myelin by the surrounding glial cells and show that it is dependent on the level of activity present in an axon. We elucidated the biological mechanisms by which astrocytes regulate this process. Our role is to use a theoretical framework to predict how the changes in myelin thickness, as well as the increase in the nodal width, affects the propagation of the signals along a myelinated axon, and to experimentally measure conduction speeds using a data analysis framework we implemented. The theoretical and experimental results were in a very good agreement. A manuscript describing these methods and ultimately the mechanism of the dynamic myelin regulation is to be submitted to eLife in July of 2017. We also developed different models of myelin plasticity, or generally, delay plasticity. We study the consequences of such adaptive time delays for two main cases: one where the plasticity is activity dependent and another where the plasticity depends on the temporal mismatch between presynaptic and postsynaptic action potentials. In the former case, we studied the effect of activity dependent adaptive time delays on the stability of the system of coupled oscillators, with its implications on the stability of the oscillations and synchrony in the brain. Two newly proposed models of delay plasticity are based on 1) temporal mismatch, akin to the spike time dependent plasticity, and 2) oligodendrocyte mediated learning. Both models were studied in the context of spiking neural networks. We show how the stability of the synchronized state in the network relies on having such adaptive delay. A manuscript describing this work is in the final stages of preparation. In a project with investigators of the Section on Behavioral Neurogenetics in the Intramural Research Program at NICHD, we study patterns of gene expression in developing zebrafish using supervised and unsupervised machine learning methods and develop a framework for automated annotation of the zebrafish brain neuroanatomy. This work will allow the production of maps with increasingly fine-grained segmentation of distinct neuronal cell types and was presented at the Annual Meeting of the Society for Neuroscience in San Diego, CA, in November of 2016. In collaboration with Dr. Basser (NICHD) and Dario Gasbarra, Ph.D. (University of Helsinki, Finland), we derived the probability distributions of the eigenvalues and eigenvectors in the case of symmetric random matrices, with isotropic matrix-variate Gaussian noise. We apply these results to diffusion tensor imaging and show how one can use Fisher information and spherical t-design to develop DTI experiments with isotropic statistical properties. A manuscript describing this work has been published in SIAM Journal on Medical Imaging in July of 2017. Together with four other principal investigators from NIH (NICHD, NINDS, and NIMH), we participated in a recently obtained R24 BRAIN Initiative grant (In Vivo Brain Network Latency Mapping). The goal of this effort is to measure the typical temporal latency information (the time delay that exists in the signaling communications between different brain regions) in a human brain, as being able to measure the latency information in vivo can provide a great diagnostic tool for a number of neuropsychiatric disorders. To achieve this, data from four different imaging modalities are utilized. These are: diffusion MRI, which provides the structural, electroencephalography (EEG), magnetoencephalography (MEG) and transcranial magnetic stimulation (TMS). In the initial stage we developed the algorithms for extracting latency information from the time series data. This work is to be presented at the Annual Meeting of the Society for Neuroscience in Washington, DC, in November of 2017.